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The sufficient utilization of the mega data acquired from operative power units is a promising way leading to high efficient, clean and safe operation of power plant. Hereinto, data filtering is a critical link to the data-driven dynamic model identification aiming at optimizing the process control. The machine learning is an advantageous approach for filtering usable data from mega databases for identification due to its effective statistical learning strategy to the field data with noises, disturbances and coupling quantities. Therefore, a data filtering method of combining Gaussian Naive Bayesian classifier and prediction error method (Gaussian NB-PEM) is proposed in this paper. Firstly, variables associated with identification model are selected by analyzing the characteristics of the process. Secondly, the GaussianNB classifier is used for coarse data filtering by calculating the priori probability of each attribute from training sample set and the probability with all possible values of the known categories for testing sample set. Thirdly, the prediction error method is used for further data filtering based on model fitting. By using the filtered closed-loop data, the dynamic characteristics of the superheated steam temperature is modeled and verified by closed-loop control simulation, showing the validity of the Gaussian NB-PEM data filtering method.

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

In this paper, the generalized detector (GD) constructed on the basis of the generalized approach to signal processing in noise is employed by synthetic aperture radar (SAR) system to define a target location. The GD is proposed to solve the common problem of target detection at the very low signal-to-noise-ratio (SNR) when the conventional detection algorithms are not able to do it. Simulation results demonstrate a GD performance superiority in comparison with the search for undefined maritime objects (SUMO) detector one.

The Cygnus Dual Beam Radiographic Facility consists of two identical radiographic sources each with a dose rating of ∼4-rad at 1 m, and a ∼1-mm diameter spot size. The development of the rod pinch diode (RPD) was responsible for the ability to meet these criteria. The RPD in each machine uses a 0.75-mm tungsten diameter tapered anode rod extended through a 9-mm diameter cathode aperture. When properly configured, the electron beam born off the aperture edge can self-insulate and pinch onto the tip of the rod creating an intense, small x-ray source. These sources are utilized in concert with an imaging system on complex experiments, which are single-shot, high-value events. In such an application there is an emphasis on machine performance (reliability and reproducibility). Vacuum quality is a significant determinator for source performance. In this paper we discuss the Cygnus Dual Beam Radiographic Facility, the associated vacuum systems, vacuum monitor calibration and a multi-purpose vacuum chamber. Vacuum system performance, using various enhancements, will be reported. Using the vacuum system enhancements, as well as other machine tweaks, recent source performance has improved. This new level of performance will be presented via dosimetry and electrical measurements.

Ontologies have been designed for the purpose of representing a domain of knowledge in a way that makes it understandable by machines and humans. Ontologies have been used in many areas in computer sciences such as knowledge engineering, semantic web, and information retrieval. Religious studies is one of the areas were ontologies are in demand. In this paper, we present the design of domain ontology about prophets and messengers in Islam from trustworthy resources: the Quran and Hadith. The ontology can be used to better understanding all life aspects of prophets and messengers in Islam. The classes, relations and properties of this special domain ontology were gathered and integrated to help researchers to identify and explore knowledge about prophets in Islam such as their lives, books and teachings, the nature of the messages they brought to their nations and tribes and much more.

Gain-cell embedded DRAM (GC-eDRAM) is an attractive alternative to traditional SRAM, due to its high-density, low-leakage, and inherent 2-ported operation, yet, its dynamic nature leads to limited retention time that requires periodic, power-hungry refresh cycles. This drawback is further aggravated in scaled technologies, where increased leakage currents and decreased in-cell storage capacitances lead to accelerated data integrity deterioration. However, the emerging approximate computing paradigm utilizes the inherent error resilience of some applications to tolerate data errors. Such error tolerance can be exploited by reducing the refresh rate in GC-eDRAM to achieve a substantial decrease in power consumption, at the cost of an increase in cell failure probability. In this paper, we present the first fabricated and fully functional GC-eDRAM in a 28 nm bulk CMOS technology. The array, which is based on a novel mixed-V t 4T bitcell, can be used in both traditional and for approximate computing applications, featuring a small silicon footprint and supporting high-performance operation. Silicon measurements demonstrate successful operation at 800 Mhz under a 900 mV supply, while retaining almost 30% lower area than a single-ported 6T SRAM in the same technology.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

The learning behavior of the agent is a challenging and interesting issue in an unknown environment. Reinforcement learning obtain the developed strategy through exploration and interaction with the environment, and the characteristic of online learning make it as an important branch of machine learning research. In this paper, we summarize the current research of the reinforcement learning applications and hotspots. Firstly, the principle, structure and the main classic algorithms of the reinforcement learning are introduced. Secondly, according to the recent research results, we introduce four main applications of reinforcement learning, namely robot, unmanned aerial vehicle, multi-agent and intelligent traffic. Finally, the research hotspots and the development direction of the reinforcement learning are introduced, which conclude the partial perception, hierarchical reinforcement learning, combination with other artificial intelligence technologies and game theory.

Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for roadside safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.

A compact microwave bandpass filter with λ/4 stepped impedance resonators (SIRs) is presented in this paper. λ/4 SIRs are employed here not only to miniaturize the circuit size, but also to eliminate spurious resonance at 2f 0 . To achieve the maximal number of finite transmission zeros, this filter is synthesized based on the 4th-order fully canonical coupling topology. As a result, four finite transmission zeroes are analytically achieved, and used to sharpen filter rejection skirts. Three capacitive J couplings are systematically designed to meet the required coupling strengths for the filter frequency response. Besides, they also adjust their coupling zeros for 3f 0 spurious suppression. Lastly, a 4th-order fully canonical bandpass filter is designed and fabricated with central frequency of 1.8 GHz and fractional bandwidth of 11.0% The proposed filter possesses preferred merits: compact size, 4 transmission zeros and up to 4.8f 0 20 dB attenuated stopband.

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.

Insurance telematics is a disruptive technology that is expected to reform the vehicle insurance industry. Based on sensor data, the traditional measures for calculating the insurance premium are complemented to determine a fee that more accurately predicts the risk profile of the policyholder. From an instrumentation and measurement point of view, there are several insurance telematics challenges that have to be tackled. It is about consistently extracting relevant figures of merit (FoMs) like number of harsh braking, speeding, heavy cornering, trip smoothness, etc, and then to transform these FoMs into a valid measure, or score, that determines the risk profile of the insurance customer. The paper presents a characterization of FoMs in terms of actuarial relevance, the driver's influence, stationarity and observability, which are important for the given task. Dedicated hardware to be fitted into the on-board diagnostics outlet as well as contemporary smartphones are two different insurance telematics platforms for data collection. The paper discusses the properties of the smartphone in the perspective of insurance telematics as a low-cost alternative to the hardwired platforms like the one connected to the vehicle's on-board diagnostics outlet.

Survey of research papers is not an easy task for novice researchers, because they are not always good at finding all appropriate keywords for the survey. Moreover, it is not easy for them to understand positions of papers in their research fields instantly, even when they use famous search engines like Google Scholar, it may often take a long time for them to find scholarly literature. On the other hand, many researchers have presented citation visualization techniques for surveying research papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. In this paper, we proposed a visualization technique for citation networks applying topic-based paper clustering. Our technique categorizes papers applying LDA (Latent Dirichlet Allocation), and constructs clustered networks consisting of the papers.

Machine vision based automatic apparatus is a necessary component of an automated production line. In this paper, taking can-end surface defect detection as an example, we presented a visual inspector and proposed a new surface defect detection framework. Firstly, the Region of Interest (ROI) of the input image is got by circle detection and prior radius knowledge of can-end. Then, the saliency map and texture filtering image are computed to overcome the problems of irregular texture and non-uniform illumination. Finally, all suspected defects are obtained by saliency map segmentation, connected component analysis. Following, the size, the average saliency value and the mean texture filtering value of each suspected defective region are calculated, and the 3-dimensional features are used to distinguish true defects in the central panel region of can-end. We employ the designed equipment to grab can-end images and do tests divided into four groups on the grabbed images. The accuracy is 98.16% and the execution time is about 65.7 milliseconds. Experimental results demonstrate that the proposed method can inspect faint and low contrast defect accurately. Meanwhile, the proposed method is simple and computationally efficient and meets the high speed of industrial automation.

This paper proposed smart agricultural framework over RCAS in CATV network. The proposed framework adopts RCAS, which is a security system which is dedicated to cable television system, as a data protection system, and cable two-way broadband network as a communication channels among smart agricultural service participants.

The massive big data transfer requirements in the cloud age are posing unprecedented challenges on todays' network infrastructure. Delivering data in a cost effective and scalable manner is of critical importance to support the continued growth of cloud services. In this paper, we revisit the problem of delivering large data on top of dynamic circuit switched networks, with a particular focus on how it can be used together with packet switching in an integrated data delivery model. We argue that the Software Defined Networking (SDN) concept and flow based switching are crucial to realize scalable and energy efficient integrated data delivery. We propose to use built-in or public storage to resolve contentions between large data flow transfers.

Design of analog and digital electronic circuits is an integral part of the electrical engineering curriculum. Software tools, which enable design, simulation, measuring and circuit analysis as well as PCB (Printed Circuit Board) design, are called EDA (Electronic Design Automation). The paper describes the introduction of EDA software in the first semester of the professional study of electrical engineering at the Zagreb University of Applied Sciences within course Personal Computers in Electrical Engineering. Examples of laboratory exercises related to electrical circuit diagrams, use of component libraries, work in standard laboratory environment (power supplies, function generators, voltmeters, ammeters, oscilloscopes, etc.), circuit analysis in a DC and AC mode as well as in time and frequency domain are described. Focus is on the optimal implementation of EDA tool NI Multisim and its integration with the existing learning management system Moodle as a platform for exercise learning and knowledge assessment.

System losses, power generation and generation cost are some of the most important factors that significantly influence the efficiency of power systems. Integrating distributed generation systems into distribution networks is one of the effective solutions. Many studies focused on finding the optimal planning of distributed generation systems to get the maximum benefits from them. In this paper, a brief recent review on a variety of heuristic optimization techniques applied in literature to optimally allocate and size distributed generations will be presented.

Event horizon imaging requires a third-generation space Very Long Baseline Interferometer on a 500 GHz band. The interferometer challenges available frequency standards. Namely, the practical coherent integration time is about 45 times shorter than needed. A self-calibration could aid this though a 3rd satellite is needed hence, an increased mission cost. This paper is devoted to experimental verification of a novel concept for two-way frequency transfer.We built a dedicated breadboard, test set-up, and run tests with and without temperature differences between branches of the breadboard. A phase difference between derived oscillators is the white noise-like function of the time with 1-σ of 0.5° at 103.2 GHz over more than 3 hours. Varying the temperature difference between branches ±3 C during 4.5 hours, being the orbit period, confirmed that the phase difference is the function of this temperature difference too. The Allan Deviation demonstrated between two derived 103.2 GHz oscillators is of 1.1×10−14/τ over 10 ms < τ < 1000 s, where τ is the averaging time, and the optical communication link path length is 5 km (17 ms). Degradation of this Allan Deviation is of 2 times at the longest-possible delay of 0.2 s in the interferometer system; the 0.2 s is the biggest sum of geometric and communication delays. This worst-case satisfies the requirement with a margin of 11 times. An obtained coherence is in the range of 0.997−0.9998 which is beneficial for the interferometer, at 557 GHz. This is one of the key technology enablers for black hole imaging from space.

Elasticity makes cloud computing an attractive platform for executing complex large-scale expensive dataflows, as it enables different trade-offs between execution time and monetary cost, by varying the number of resources to be provisioned. With cloud providers offering heterogeneous types of resources with different performance and price characteristics, the problem of identifying the various trade-offs available is a great challenge, as the number of possible alternative configurations increases significantly compared to a homogeneous environment, which is itself already computationally difficult. This paper proposes a novel algorithm for dataflow scheduling on heterogeneous clouds that identifies solutions (schedules) close to the optimal pareto front, by exploring the search space in an efficient way. The results of an experimental comparison with the state of the art show that, in several cases, the proposed algorithm provides a richer, more diverse set of solutions, several of which are characterized by significantly better time-money trade-offs.

This paper evaluates a wristband-type tactile interface as a user interface for controlling an industrial robot remotely. When operating a remote monitoring system, information delivery from a local site to an operator at a remote site is important. Visual and auditory feedback is often used. However, perception of the information might be limited if different kinds of information in particular sense modality is delivered at the same time. To solve the problem, we proposed a tactile interface which uses tactile information and we evaluate it. First, we explain the remote operation system for industrial robot and interface, then after that, we present an experiment of vibrotactile information of industrial robot.

Based on the principle of piezoelectric driving and ultrasonic near-field levitation technology, this paper designs a piezoelectrically driven ultrasonic levitation bearing, which is mainly composed of a rectangular piezoelectric sheet and a metal cylinder. The piezoelectric sheet drives cylinder wall to squeeze air in bearing gap under the action of high-frequency alternating voltage to form a gas squeezed film. According to the theory of viscous fluid mechanics, squeeze film Reynolds equation is derived. From the equation, it can be seen that cylinder radial deformation and squeeze number are the key parameters that determine the bearing capacity of gas squeeze film. With finite element software, radial deformation displacement function of cylinder is obtained, and the relationship between squeeze number and bearing capacity is analyzed by solving Reynolds equation. Based on above simulation and theoretical analysis, bearing is studied experimentally. The experimental results prove that theoretical calculation method is accurate, which has guiding significance for theoretical research and structural design of ultrasonic levitation bearings.

The paper presents a novel short-range wireless sensor node architecture, based on Integral Pulse Frequency Modulator (IPFM) and Ultra-Wideband (UWB) pulse generator. Due to the lack of internal clock signal source and multi user implementation without application of a microprocessor, the architecture is simple and energy efficient. Multi-user coding is performed using delay elements, where each user has unique delay time value. The output of the IPFM is fed to the delay element. Delayed and original signal from the IPFM are feeding UWB pulse generator. The paper presents the transmitter architecture and measurements of the transmitter signals in time and frequency domain.

Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.

In this paper we propose a Sliding Mode Controller for a class of scalar bilinear systems with delay in both the input and the state. Such a class is considered since it has shown to be suitable for modelling and control of a class of turbulent flow systems. The stability and robustness analysis for the reaching phase in the controlled system are Lyapunov-based. However, since the sliding dynamics is infinite dimensional and described by an integral equation, we show that the stability and robustness analysis is simplified by using Volterra operator theory.

The high energy consumption has become one bottleneck in the development of the data centers (DCs), where the main energy consumers are the cooling system and the servers. Therefore, the joint optimization for the energy efficiency of the cooling system and the servers is a crucial problem, while most of previous works on energy saving only studies one of these two components in an isolated manner. In this paper, we propose a real-time strategy, rTCS (real-time Task Classification and Scheduling strategy), to jointly optimize the energy efficiency of these two components in the scenario where the tasks arrive dynamically. Strategy rTCS first labels the tasks to classify them according to their run time and end time with a time complexity of O(1) and a bounded space complexity. Then, rTCS schedules the tasks in real time based on their labels and the energy consumption model of the DC. Simulation results show that rTCS can effectively improve the energy efficiency of DCs.

Reliability and fault-tolerance are essential requirements of critical, autonomous computing systems. In this paper, we propose a methodology to quantify, and maximize, the reliability of computation in the presence of transient errors when considering the mapping of real-time tasks on an homogeneous multiprocessor system with voltage and frequency scaling capabilities. As the likelihood of transient errors due to radiation is environment- and component-specific, we use machine learning to estimate the actual fault-rate of the system. Furthermore, we leverage probability theory to define a trade-off between power consumption and fault-tolerance. If a processing element fails, our methodology is able to re-map the application, establishing whether the real-time requirements will still be met, and how reliable the new, impaired system will be. Results show that the proposed methodology is able to adjust mapping and operating frequencies in order to maintain a fixed level of reliability for different fault-rates.

Due to the existing cryptographic schemes based on the satisfiability problem (SAT) lack the specific security model and the security analysis of the provable security theory, the security is questioned. For this problem, combining with the advantages of SAT and Elgamal scheme, we construct a secure, verifiable encryption scheme in this paper. Firstly, combining and improving the SRR(N, k, s) model based on the difficulty problem of the random regular formula near the phase transition point, we construct an encryption algorithm. Secondly, on the basis of the existing Damgârd Elgamal scheme, a more secure scheme based on SAT and variants of discrete logarithm problem is proposed. Lastly, the analysis results show that compared with the existing SAT-based hybrid cryptographic schemes, the private key of the hybrid encryption scheme proposed in this paper is shorter and the ciphertext satisfies the verifiability, and the scheme can secure against adaptive chosen ciphertext attacks (CCA2).

The paper studies the robust control problem of discrete-time systems with state saturation. First the paper introduces an ellipsoid and supposes that the invariant set of the system contains the ellipsoid. By using matrix norm theory we obtain one sufficient condition of the stability of the discrete-time system with state saturation. The attraction domain of the system is estimated. Then the paper researches the stability of the uncertain system with state saturation. Finally a numerical example is presented to demonstrate the result of the paper.

Enabling software systems to adjust their protection in continuously changing environments with imperfect context information is a grand challenging problem. The issue of uncertain reasoning based on imperfect information has been overlooked in traditional logic programming with classical negation when applied to dynamic systems. This paper sketches a non-monotonic approach based on Answer Set Programming to reason with imperfect context data in adaptive security where there is little or no knowledge about certainty of the actions and events.

Malignant melanomas are the most deadly type of skin cancers however detected early enough give a high chances for successful treatment. The last years saw the dynamic growth of interest of automatic computer-aided skin cancer diagnosis. Every month brings new research results on new approaches to this problem, new methods of preprocessing, new classifiers, new ideas to follow etc. In particular, the rapid development of dermatoscopy, image processing methods, as well as the ever-increasing computing power of computers caused that researchers are able to consider significantly more features of the analyzed lesion than has been done so far using methods recognized in a medical community such as ABCD or Menzies methods. From the other hand more features not always imply an improvement in terms of efficiency of the diagnosis and its transparency. Hence, in this paper we survey the kind of features taken into account by the researchers and then, selected the most efficient set of them. Proposed method jointly selects the optimal set of features representing the analyzed lesion together with the accompanying form of the neural classifier (number of neurons, activation functions). The evolutionary algorithms are used in order to carry out the optimization. Obtained results are even better than the ones obtained by the most efficient these days deep classifiers.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research., Accepted for publication at 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020

In recent years, chaos-based image cipher has been widely studied and a growing number of schemes based on permutation-substitution architecture have been proposed. To better meet the challenge of real-time secure image communication applications, this paper suggests a new image encryption scheme using a parallel substitution. In the permutation stage, the Arnold cat map is employed to shuffle the pixel positions so as to erase the strong relationship between adjacent pixels. In the substitution stage, the scrambled image is firstly decomposed into eight bit-planes which are then parallelly mixed with keystreams generated by the chaotic logistic map. Theoretically, the parallel substitution strategy runs eight times faster than the serial strategy on an 8-thread processor as the volume of data processed by each substitution unit is 1/8 of that of the input image. Experimental results show that the proposed parallel scheme runs more than five times faster than the serial scheme. Extensive security analysis is carried out with detailed analysis, demonstrating the satisfactory security of the proposed scheme.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus

Launched in 2018, NASA's Parker Solar Probe (PSP) mission has been described as “The Mission to Touch the Sun”. Over a 7 year prime mission, the spacecraft will make 24 close encounters with the Sun. In order to meet requirements, PSP's telecom subsystem needed to incorporate a Ka-band downlink for radiometrics and science data return. PSP uses a 0.6 meter High Gain Antenna (HGA). This antenna is used only for a Ka-band downlink, and during its use is nominally the only time the science data is transmitted. Aspects of the mission design also restrict the use of the HGA to short periods in between each solar flyby. These constraints require the RF team and Mission Operations team to be extra diligent in optimizing the downlink data return. This paper provides a summary of PSP's first year of operations at Ka-band. In addition to presenting the overall Ka-band performance, other items that will be focused on include: planning considerations for Ka-band coverage with the DSN, observed effectiveness of a HGA calibration, pre-launch link assumptions, and possible future work. With multiple future deep space missions utilizing a Ka-band downlink, this information will hopefully be beneficial to help with planning and optimizing the data return.

Identifying butterfly species by image patterns is a challenging task in computer vision and pattern recognition community due to many butterfly species having similar shape patterns with complex interior structures and considerable pose variation. In additional, geometrical transformation and illumination variation also make this task more difficult. In this paper, a novel image descriptor, named Gaussian convolution angle (GCA) is proposed for butterfly species classification. The proposed GCA projects the butterfly vein image function and intensity image function along a group of vectors that start from a common contour points and ends at the remaining contour points which results a group of vectors that capture the complex vein patterns and texture patterns of butterfly images. The Gaussian convolutions of different scales are conducted to the resulting vector functions to generate a multiscale GCA descriptors. The proposed GCA is not only invariant to geometrical transformation including rotation, scaling and translation, but also invariant to lighting change. The proposed method has been tested on a publicly available butterfly image dataset that has 832 samples of 10 species. It achieves a classification accuracy of 92.03% which is higher than the benchmark methods.

Cable accessories made of ethylene-propylene-diene terpolymer (EPDM) are considered to be the weakest part of HVDC cable system due to the existence of the interface between cable insulation and itself. The charges are likely to accumulate at the interface between insulation materials with different conductivity and permittivity, which may induce the occurrence of partial discharge and breakdown. Nanoparticles can be applied to adjust the interface charge behaviors. In this paper, carbon black (CB) nanoparticles were dispersed into EPDM with 0, 0.5, 1, 3 and 5 wt% respectively. The effects of nanoparticle doping on the interface charge distribution of LDPE and EPDM composite insulation were measured under −15 kV/mm. Furthermore, dielectric properties and DC conduction were introduced to discuss the suppression mechanism of carbon black nanoparticle doping. Obtained results the interface charge density can be suppressed to 0.5 C/m3 with 1 wt% carbon black nanoparticle doping, much less than the undoped and other doped groups. The mechanism of interface charge suppression is attributed to the decrease of conduction and permittivity mismatch by carbon black nanoparticle doping, which was proved by the dielectric spectroscopy and conduction current tests. The interface charge buildup depends on the time constant controlled by the doping proportion.

The objectives set for smart grids are as diverse as they are exciting and ambitious. Instead of overloads, bottlenecks and blackouts, smart grids will ensure the reliability, sustainability and efficiency of the Electric Vehicle Supply Equipment (EVSEs). Therefore, EVSEs and Electric Vehicles (EVs) must integrate wireless communication between them to discover the availability and make pre-reservations of charging time slots. In this paper, we propose a novel multi-objective EV Charging Slots Assignment (CSA) optimization model that, besides balancing energy usage between EVSEs, minimizes the latency time of EVs. Moreover, we integrate our optimization model to a communication protocol between EVs and EVSEs that allows a reliable reservation process, called Reliable Broadcast for EV Charging Assignment (REBECA) [1]. Then, we propose a centralized heuristic to efficiently solve our model as an offline CSA process.

In order to sell items of a genre that each user is not interested in, their latent interests of the genre have to be induced. Therefore, we focus on "Influencer," who is the person that has a large impact on a user's behaviors, and research a recommender system which advertises some items by utilizing influencer's remarks via social media. This paper investigates what kinds of remarks and remarkers would induce users'interests. As the result, we have revealed many findings. One is that "One of the factors that would induce the users' positive interests on the movie, is the positive reputation for the movie that is included in tweets." The other is that "If the user does not like the remarker, the user's negative interests for the items in the remarks of the person would be induced, even though the person has famousness.", INSPEC Accession Number: 18674306

In this paper, a new procedure to produce broadside circular polarization (CP) in modified rectangular dielectric resonator antenna (DRA) has been presented. The dielectric resonator is deformed periodically in stairshaped form along anti-clockwise direction. The concept of perfect magnetic boundary conditions at the interface of air and dielectric resonator has been successfully applied in this work. Final proposed antenna shows input impedance bandwidth and axial ratio bandwidth of 35% (3.34 – 4.76 GHz) and 8.5% (3.70- 4.03 GHz) respectively.

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more effectively than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the state-of-the-art on UCF101 and HMDB datasets., Comment: Accepted in WACV 2017

This paper reports a concise complexity analysis for the multilevel fast multipole algorithm. The algorithm can solve a plethora of problems governed by different kinds of physics, and therefore the analysis is done for surface and volume problems in two and three dimensions for the sake of generality.

This paper presents an idea to further simplify and relax the linear matrix inequality (LMI) stability conditions of Takagi-Sugeno (T-S) fuzzy systems. By considering the distribution of membership functions in a unified space, we can easily find the overall relation of the original nonlinear system and its approximated local subsystems. Based on the theory of convex combination, the upper-bounds and lower-bounds of each membership functions can be directly used to construct less conservative LMI conditions. The cases of both Type-1 and Type-2 systems are considered, and examples are provided to illustrate the achieved improvement.

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.

User stories are descriptions of functionality that a software user needs. They play an important role in determining which software requirements and bug fixes should be handled and in what order. Developers elicit user stories through meetings with customers. But user story elicitation is complex, and involves many passes to accommodate shifting and unclear customer needs. The result is that developers must take detailed notes during meetings or risk missing important information. Ideally, developers would be freed of the need to take notes themselves, and instead speak naturally with their customers. This paper is a step towards that ideal. We present a technique for automatically extracting information relevant to user stories from recorded conversations between customers and developers. We perform a qualitative study to demonstrate that user story information exists in these conversations in a sufficient quantity to extract automatically. From this, we found that roughly 10.2% of these conversations contained user story information. Then, we test our technique in a quantitative study to determine the degree to which our technique can extract user story information. In our experiment, our process obtained about 70.8% precision and 18.3% recall on the information.

This paper presents load balancing technique of 3 phase system at consumer load and rearrangement of the installation at unit level itself. It will help load balance between phases.Also reduction of electricity bills. Automatic balancing is based on a switching technique following certain equations & constraints. This method can be used on both feeders & unit level. In this paper load unbalancing of three phase system is implemented in MATLAB & load is balanced with respect to algorithm.

In this paper, a risk embedded two-stage stochastic optimization model is proposed to optimize the scheduling of the AC/DC comprehensive energy network (CEN). In the day-ahead scheduling, based on the forecast photovoltaic (PV) output and various loads, the first stage optimization model is established to minimize the operation cost in a daily time interval. In the real-time scheduling, considering that the PV output and loads are stochastic, the scenarios reduction based Monte Carlo method is used to generate multiple scenarios, and the second stage stochastic optimization model is adopted to minimize the weight sum of the expected deviation costs in the scenarios and the corresponding variance. A case of the CEN system has been studied, and results have been compared to verify the effectiveness of proposed method.

Discriminant Analysis is utilised in working out which specific classification, a data pertains to on the basis of its needed features. Linear Discriminant Analysis(LDA) achieves the maximum class separability by projecting high-dimensional data onto a lower dimensional space. However, LDA suffers from small sample size(SSS) problem where the dimensionality of feature vector is very large compared to the number of available training samples. Regularized Discriminant Analysis(RDA) handles SSS problem of LDA with an introduction of regularization parameter(λ) and has the ability to reduce the variance. One important issue of RDA is how to automatically estimate an appropriate regularization parameter. In this paper, we propose a new algorithm to enhance the performance of RDA by effectively estimating an appropriate regularization parameter in order to reduce training time and error rate. Experiments are done using various benchmark datasets to verify the effectiveness of our proposed method with the state-of-the-art-algorithm.

This paper presents a control design for the irrigation station by sprinkling. The proposed method is applied in order to solve the problem of managing water sources and distributions systems. The main contribution is focused primarily on conceiving T-S fuzzy model for the irrigation station. In formalism of the fuzzy-coalescence algorithm, we derive sufficient conditions to determine the model parameters. Meanwhile, additional effort is developed to achieve PI control for the irrigation station model.

Password security of user accounts is a major concern in today's fast moving and advanced computing world with password authentication the most commonly used identification mechanism. Its security can be enhanced using typing biometrics as a transparent layer of user authentication. This paper describes a password hardening mechanism that is able to recognize different users based on their time latencies. Our research focuses on only using the time period between keystrokes as the measure of the individual's typing pattern. Seven individuals used the same 7-digit password which allowed the system to record the time interval between each digit. Identification improvements up to 3% were achieved with Subtractive Clustering Fuzzy Inference when rule conflicts were removed.